Data was filtered for reactions with “Call” == “Pass”. Culture condition was added to each sample to facilitate downstream analysis.
These are samples that are not among the original sample set that were used for RNA-seq. Note the inclusion of the blank samples for the target Srp68. This indicates that the Srp68 primers amplify without template. Possible primer-dimer amplification. The Srp68 reactions should be excluded.
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = corr_data %>% filter(culture ==
## "AN"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.51175 -0.16423 -0.05963 0.03238 1.78971
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05925 0.04592 1.29 0.202
## seq_lfc 1.11003 0.05945 18.67 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3296 on 56 degrees of freedom
## Multiple R-squared: 0.8616, Adjusted R-squared: 0.8591
## F-statistic: 348.6 on 1 and 56 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = corr_data %>% filter(culture ==
## "AE"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -7.2250 -0.4718 0.0510 0.6835 3.4501
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.3543 0.2166 1.636 0.107
## seq_lfc 1.2791 0.1964 6.514 2.19e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.649 on 56 degrees of freedom
## Multiple R-squared: 0.4311, Adjusted R-squared: 0.4209
## F-statistic: 42.43 on 1 and 56 DF, p-value: 2.193e-08
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = corr_data %>% filter(culture ==
## "AEN"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.2054 -0.3782 -0.0664 0.2530 3.0005
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.34362 0.11060 3.107 0.00297 **
## seq_lfc 1.08462 0.04659 23.282 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.8358 on 56 degrees of freedom
## Multiple R-squared: 0.9064, Adjusted R-squared: 0.9047
## F-statistic: 542 on 1 and 56 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = uncor_corr_data %>%
## filter(culture == "AN"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.57507 -0.16617 -0.05160 0.04665 1.79807
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.05265 0.04669 1.128 0.264
## seq_lfc 1.11889 0.06089 18.374 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.3342 on 56 degrees of freedom
## Multiple R-squared: 0.8577, Adjusted R-squared: 0.8552
## F-statistic: 337.6 on 1 and 56 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = uncor_corr_data %>%
## filter(culture == "AE"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.77218 -0.05822 0.02721 0.09987 0.37234
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.05990 0.02485 -2.41 0.0193 *
## seq_lfc 1.02271 0.01166 87.68 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1859 on 56 degrees of freedom
## Multiple R-squared: 0.9928, Adjusted R-squared: 0.9926
## F-statistic: 7688 on 1 and 56 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = mean_ddCt ~ seq_lfc, data = uncor_corr_data %>%
## filter(culture == "AEN"))
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.86008 -0.20248 -0.08165 0.05933 1.73687
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.21707 0.05683 3.82 0.000337 ***
## seq_lfc 1.00193 0.02118 47.30 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4268 on 56 degrees of freedom
## Multiple R-squared: 0.9756, Adjusted R-squared: 0.9751
## F-statistic: 2238 on 1 and 56 DF, p-value: < 2.2e-16
## R version 4.1.3 (2022-03-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur/Monterey 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] plotly_4.10.1 ggplot2_3.4.2 stringr_1.5.0 tidyr_1.3.0 dplyr_1.1.2
## [6] readxl_1.4.2
##
## loaded via a namespace (and not attached):
## [1] tidyselect_1.2.0 xfun_0.39 bslib_0.4.2 purrr_1.0.1
## [5] splines_4.1.3 lattice_0.21-8 colorspace_2.1-0 vctrs_0.6.2
## [9] generics_0.1.3 htmltools_0.5.5 viridisLite_0.4.1 yaml_2.3.7
## [13] mgcv_1.8-42 utf8_1.2.3 rlang_1.1.0 jquerylib_0.1.4
## [17] pillar_1.9.0 glue_1.6.2 withr_2.5.0 RColorBrewer_1.1-3
## [21] lifecycle_1.0.3 munsell_0.5.0 gtable_0.3.3 cellranger_1.1.0
## [25] htmlwidgets_1.6.2 evaluate_0.20 labeling_0.4.2 knitr_1.42
## [29] fastmap_1.1.1 crosstalk_1.2.0 fansi_1.0.4 highr_0.10
## [33] scales_1.2.1 cachem_1.0.7 jsonlite_1.8.4 farver_2.1.1
## [37] digest_0.6.31 stringi_1.7.12 grid_4.1.3 cli_3.6.1
## [41] tools_4.1.3 magrittr_2.0.3 sass_0.4.5 lazyeval_0.2.2
## [45] tibble_3.2.1 pkgconfig_2.0.3 ellipsis_0.3.2 Matrix_1.5-4
## [49] data.table_1.14.8 rmarkdown_2.21 httr_1.4.5 rstudioapi_0.14
## [53] R6_2.5.1 nlme_3.1-162 compiler_4.1.3